Evaluation of OLI Landsat-8 images based on spectral indices in detecting areas affected by mining tailings mud: a case study of the Brumadinho dam rupture, Brazil

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorLucchetta, Beatriz Cirino-
Autor(es): dc.creatorWatanabe, Fernanda Sayuri Yoshino-
Autor(es): dc.creatorOliveira, Fernanda Silva-
Data de aceite: dc.date.accessioned2025-08-21T20:37:51Z-
Data de disponibilização: dc.date.available2025-08-21T20:37:51Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1590/s1982-21702024000100013-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306073-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306073-
Descrição: dc.descriptionThe socio-environmental impacts caused by the collapse of a mining dam can be irreversible. In Brazil, the dam collapse at the Córrego do Feijão Mine was considered one of the worst disasters in the country. Remote sensingbased approaches have been used to detect and monitor areas affected by tailings from dam rupture. Therefore, it was proposed to identify the area affected by mining tailing mud, in Brumadinho, Minas Gerais State, through the analysis of three different spectral indices: Normalized Difference Vegetation Index (NDVI), Ferrous Minerals Ratio (FMR) and Clay Minerals Ratio (CMR). These indices were computed from the Operational Land Imager (OLI) images of Landsat-8. Different thresholds were tested to define the best range for delineating the affected area. For validation, the limits of the affected area, obtained from a higher resolution sensor, GeoEye-1, were used as reference. The methodology demonstrated great potential for detecting areas affected by dam failure. The indices NDVI and FMR delimited the area of interest with high performance, with precision varying between 95% and 92%; recall between 88% and 87%; F-score between 91% and 89%; and global accuracy between 84% and 80%, showing to be suitable mapping such disasters.-
Descrição: dc.descriptionPost-Graduation Program in Cartography Sciences School of Sciences and Technology São Paulo State University (UNESP), São Paulo State-
Descrição: dc.descriptionDepartment of Cartography School of Sciences and Technology São Paulo State University (UNESP), São Paulo State-
Descrição: dc.descriptionGraduation in Environmental Engineering School of Sciences and Technology São Paulo State University (UNESP), São Paulo State-
Descrição: dc.descriptionPost-Graduation Program in Cartography Sciences School of Sciences and Technology São Paulo State University (UNESP), São Paulo State-
Descrição: dc.descriptionDepartment of Cartography School of Sciences and Technology São Paulo State University (UNESP), São Paulo State-
Descrição: dc.descriptionGraduation in Environmental Engineering School of Sciences and Technology São Paulo State University (UNESP), São Paulo State-
Idioma: dc.languageen-
Relação: dc.relationBoletim de Ciencias Geodesicas-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCórrego do Feijão Mine-
Palavras-chave: dc.subjectEnvironmental disasters-
Palavras-chave: dc.subjectGeoprocessing-
Palavras-chave: dc.subjectIron ore-
Palavras-chave: dc.subjectMultispectral images-
Palavras-chave: dc.subjectRemote sensing-
Título: dc.titleEvaluation of OLI Landsat-8 images based on spectral indices in detecting areas affected by mining tailings mud: a case study of the Brumadinho dam rupture, Brazil-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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